Yap-Peng Tan

Computer Vision and Pattern Recognition · 2003–2018

A researcher in the Science corpus: 6 papers; trusts 536 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 141 citations into it; #6,854 of 21,575 by trust here, #6,426 by citations
• Artificial Intelligence — 51 citations into it
• Representation & Self-Supervised Learning — 26 citations into it; #2,370 of 3,145 by trust here, #2,404 by citations
• Signal Processing — 23 citations into it; #2,227 of 7,572 by trust here, #1,629 by citations
• Model Architectures — 20 citations into it
• Media Technology — 12 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Biomedical Engineering — 7 citations into it; #5,796 of 10,734 by trust here, #6,932 by citations

Whom Yap-Peng Tan trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. David G. Lowe
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Kaiming He
6. Yann LeCun
7. Karen Simonyan
8. Diederik P. Kingma
9. Ross B. Girshick
10. Ilya Sutskever

Who trusts Yap-Peng Tan overall

1. Jiwen Lu
2. Albert Gordo
3. Mei Wang
4. Plamen Parvanov Angelov
5. Xiangyu Zhu

Papers from Yap-Peng Tan's perspective

Ordered by how strongly the researchers Yap-Peng Tan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Discriminative Deep Metric Learning for Face Verification in the Wild (2014) — 738 citations
• Deep transfer metric learning (2015) — 278 citations
• Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks (2018) — 29 citations
• Neighborhood repulsed metric learning for kinship verification (2012) — 468 citations
• Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011) — 350 citations
• Color filter array demosaicking: new method and performance measures (2003) — 352 citations